Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Dr. Osama Mohammed

First Advisor's Committee Title

committee chair

Second Advisor's Name

Dr. Xia Jin

Second Advisor's Committee Title

committee member

Third Advisor's Name

Dr. Ahmed Ibrahim

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Dr. Ahmed Elsayed

Fourth Advisor's Committee Title

committee member

Fifth Advisor's Name

Dr. Stavros Georgakopoulos

Fifth Advisor's Committee Title

committee member

Keywords

electrical and electronics, power and energy

Date of Defense

12-26-2021

Abstract

Electric Vehicles (EVs) are considered one of humanity's greatest hopes to combat the climate change crises in light of their great potentials to reduce Greenhouse Gases (GHG) emissions from two main sources: the electric power industry and the fossil-based transportation sector. To help expedite the large-scale adoption of EVs on the roads, optimal solutions are needed to overcome the technical and operational barriers that face the electrical network. This is due to the introduction of significant load levels from EVs; a substantial number is expected during peak demand hours. This dissertation addresses the various interaction between different parts of the electrical system in a hierarchical optimization framework to ensure proper large-scale integration of electric vehicles; without harm to the grid or the user. To achieve our goals of achieving optimum operation scenarios, we developed a tri-level centralized and decentralized optimization methodologies with smart coordination algorithms. This will ensure optimal decisions with the simplest required communication infrastructure. Specifically, information from the EVs’ owners are collected by an aggregator located at the charging station. In a timely fashion, the aggregator sends the most updated scheduling information to its assigned microgrid that ensures no violation occurs within its jurisdiction and establishes a pricing signal for each aggregator. The microgrid takes a decision based on the downstream input information from other viii aggregators attached to it and upstream input information from a system operator that provides additional energy if needed and update the microgrids based on the overall grid’s operation. Additionally, we developed a two-stage optimization strategy to ensure proper EVs charging and discharging coordination considering voltage and reactive power control levels. The optimization strategy starts with the decomposition of the power distribution network into optimal partitions based on their voltage sensitivity levels, then solves a centralized energy coordination problem using mixed-integer linear programming. The optimization problem takes into consideration various aspects of the systems’ operation that include reactive power compensation devices and active power curtailment of PV inverters. The developed solutions presented in this dissertations have been verified and tested experimentally.

Identifier

FIDC009712

Available for download on Wednesday, March 22, 2023

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